Annotation Server Research Articles

In silico Identification and Characterization of Novel Drug Targets in Treponema denticola (strain ATCC 35405 / DSM 14222 / CIP 103919 / JCM 8153 / KCTC 15104): A Subtractive Genomics Approach

Treponema denticola is a gram-negative, highly drug resistant bacterium found in primary dentition infections and around teeth. It causes inflammation and tissue homeostasis, linked to periodontal diseases like earlyonset periodontitis, necrotizing ulcerative gingivitis, and acute pericoronitis. Research is needed to develop powerful, cost-effective, secure, and environmentally friendly antibiotics and techniques to control and manage infections caused by this bacterium. This study exploits the sophisticated in silico subtractive genomics approach to investigate potential therapeutic targets that are exclusive to the pathogen T. denticola. The full sequencing data of the Treponema denticola (strain ATCC 35405 / DSM 14222 / CIP 103919 / JCM 8153 / KCTC 15104) proteome has facilitated the computational analysis of its genome. Our analysis found 126 proteins of the pathogen that had no resemblance to the human genome. The Database of Essential Genes (DEG) identified 12 bacterial proteins which were indispensable to the pathogen, while the KEGG Automated Annotation Server (KAAS) found in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database analysis of the non-homologous proteins revealed 11 T. denticola enzymes that can be targeted for drug development. Moreover, based on sub-cellular localization prediction, all selected proteins were cytoplasmic proteins and the no outer membrane proteins were present among the non-homologous proteins, and virulent protein predictions revealed no virulent proteins among the selected proteins. Therefore, eleven proteins were selected based on their involvement in unique metabolic pathways inside the pathogen that were not present in the host. The study further revealed the protein-protein interactions of these eleven essential proteins, and successfully anticipated, assessed, and verified the three-dimensional structures of these proteins. Undertaking a screening process to detect functional inhibitors for these newly revealed targets may lead to the discovery of novel therapeutic drugs that can effectively combat Treponema denticola. CBMJ 2024 July: vol. 13 no. 02 P: 251-264

DiSSCover the Potential of FAIR Digital Object Annotations and How You Can Use Them!

The infrastructure for the Distributed System of Scientific Collections (DiSSCo) is in full development. Work within the DiSSCo Transition Project has been focused on building infrastructure, creating data models, and setting up Application Programming Interfaces (APIs) (Koureas et al. 2024). In the past years, DiSSCo has presented this work at different Biodiversity Information Standards (TDWG) conferences (Leeflang and Addink 2023, Leeflang et al. 2022, Addink et al. 2021). In this year’s session, we would like to focus on the human-facing application: DiSSCover. DiSSCover is the graphical user interface through which users can interact with Findable, Accessible, Interoperable and Reusable (FAIR) Digital Objects (FDOs), facilitating the curation and enhancement of specimen data (Islam 2024). Development started in 2022 and is ongoing. The interface acts as a gateway into the DiSSCo infrastructure, providing access to digital specimens and media. Extracted from the core DiSSCo API, the data is converted into an easily readable format and made discoverable through a diverse set of filters. DiSSCover’s main focus is to allow users to make annotations upon the data. Through the concept of annotations, we connect expert and machine-generated information, to create extended digital specimens (Hardisty et al. 2022), e.g, by creating linkages to other infrastructures, correcting or adding new information, or by triggering machine annotation services. Machine annotation services are automated scalable tools that run in the background and automatically curate and extend the specimen (Addink et al. 2023). Human users will remain important, as all annotations made by machine annotation services can be reviewed by a trusted person. Annotations are Fair Digital Objects and target a specific part of a specimen, be it a data fragment or an associated media file. At the heart of DiSSCover lies the Open Digital Specimen data specification (Leeflang and Addink 2023). It tries to harmonise multiple data standards into one generic specification based on the new Global Biodiversity Information Facility (GBIF) Unified Model (Robertson et al. 2022). The data is stored as JavaScript Object Notation (JSON) based on JSON Schemas (Anonymous 2024). Annotations are linked to specific data attributes using a JSON-path as the identifier. Data attributes can be individual terms, collections of terms called classes, or the whole object. This creates a flexible but complex data structure, the basis for which we used the World Wide Web Consortium (W3C) web annotation data model (Sanderson et al. 2017). The W3C Web annotation data model contains two main components: the target and body. The target specifies which data attribute the annotation is made on, for example, the term: ‘ods:specimenName’. This is a local term within the open Digital Specimen namespace (ods), which holds the accepted name of the digital specimen. The annotation body holds the value(s) that are appended to the digital specimen, and differ based upon the annotation motivation. DiSSCo recognises five different annotation motivations: addition, modification, comment, assessment and deletion, each of which has its own unique function. This creates a flexible structure that should be able to handle any information the user wants to add to the object. The challenge of DiSSCover is to preserve the complex structure of annotations, whilst making it convenient for users to work with. The session will provide a look at the different kinds of annotations and their use from a practical perspective. A demonstration of DiSSCover will show how users can create annotations, providing knowledge about the process that will give shape to DiSSCo’s main goal of enriching natural history data.

Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supply

With the development of 5G/6G communication networks, the industrial Internet of Things (IIoT) industry has generated a massive amount of data, presenting opportunities for advancements in the field of machine learning. The core of machine learning, labeled datasets, requires qualities such as diversity, quantity, and quality. However, the current collection of training datasets is mostly centralized, relying on crowdsourcing systems as platforms. Hence, there are four open challenges existing: (1) traditional crowdsourcing systems are mostly based on centralized platform which often suffers single point of failure, mischief attacks, DDoS attacks and are easy to be remotely hijacked; (2) the transparency of traditional crowdsourcing systems cannot be guaranteed, which results in the unfairness in assigning task or rewarding worker; (3) the quality of labeled training dataset cannot be guaranteed as the workers in traditional crowdsourcing systems are professional or non-professional; (4) the privacy of labels cannot be preserved as the platform can also learn the labeled result. In this paper, we utilize web 3.0 technology to propose a blockchain-based labeled training dataset supply system which can simultaneously supply annotation service and labeled training dataset with the challenges above overcome. Meanwhile we design a privacy-preserved truth discovery suitable for categorical data by combining it with Software Guard Extensions (SGX). Furthermore, we design a fair rewards distribution mechanism which is based on reputation system and Shapley value. The two mechanisms above can ensure the quality of labeled training dataset. Finally, to demonstrate the practicability of our design, we implement a prototype of the system deployed on Fabric test network and conduct extensive simulations. Compared to applications on public chains, the throughput of our blockchain application can reach 53 times higher.

Whole genomic sequence of Salmonella Paratyphi B isolated from a hematology patient in the northern region of Brazil

Salmonella Typhi is a zoonotic pathogen responsible for a life-threatening bloodstream infection that is common in places with limited sanitation and hygiene. This study aimed to characterize the resistance genes and sequence type (ST) of Salmonella Paratyphi isolated from a patient with acute myeloid leukemia (AML) undergoing treatment at Fundação HEMOAM, in the north of Brazil. Phenotypic identification and antimicrobial susceptibility testing were performed using the VITEK-2 system. The genomic DNA was sequenced in Illumina® equipment. Resistance genes were identified through the Genome Annotation Service. The microbiological examination of the blood culture identified S. Paratyphi B. Regarding resistance genes, aminoglycoside acetyltransferase [aac(6')-Iaa] mutations in the Gly133Glu position of GyrA and Ile249Met of ParC were identified. Multilocus sequence typing detected the ST 313 sequence type. Salmonella Paratyphi B isolated from the acute myeloid leukemia patient showed cross-resistance with other antibiotics such as quinolones and tetracyclines, which resulted from mutations in the quinolone resistance-determining region and efflux pump systems.

Whole Genome Sequence for Klebsiella pneumoniae isolate from Burn Skin

Klebsiella pneumoniae is a type of gram-negative bacterium that was initially discovered and isolated by Carl Friedlander in the year 1882. Klebsiella pneumoniae is responsible for a range of nosocomial and community-acquired illnesses, including urinary tract infections (UTIs), pneumonia, surgical site infections, and bloodstream infections. According to a number of studies, gram-negative sepsis ranks as the second most prevalent cause. A comprehensive analysis of the entire genome of Klebsiella pneumoniae was conducted using Illumina sequencing by synthesis (SBS) technology, specifically the Illumina HiSeq platform. The genetic material of the organism was comprised of a single circular chromosome of three thousand base pairs in length, exhibiting a GC content of 55.90%. In total, the genome exhibited a collective count of 164 genes associated with transfer RNA, 9 genes associated with ribosomal RNA, and 11,132 protein-coding sequences. Moreover, the analysis of genetic diversity entailed the application of sequence data, which was then compared to the reference genome that shared the highest degree of similarity. The genome annotation service offered by PATRIC and the RAST annotation system have successfully detected many antimicrobial resistance elements, clusters of genes associated with antibiotic resistance, as well as efflux pumps. This study represents the first known case of conducting a comprehensive genome sequencing of Klebsiella pneumoniae within the geographic area of Iraq, based on our current knowledge. The current investigation holds potential for advancing our comprehension of the antibiotic resistance traits demonstrated by Klebsiella pneumoniae, therefore enabling the healthcare facility to efficiently handle and alleviate outbreaks.

Whole genome sequence and comparative genomics analysis of multidrug-resistant Staphylococcus xylosus NM36 isolated from a cow with mastitis in Basrah city

BackgroundStaphylococcus xylosus is a coagulase-negative, gram-positive coccus that is found in the environment and as a commensal organism on the skin and mucosal surfaces of animals. Despite the fact that S. xylosus is considered a nonpathogenic bacterium, several studies have linked S. xylosus to opportunistic infections in both animals and humans. During an investigation of mastitis-causing agents in the governorate of Basrah, Iraq, we identified an antibiotic-resistant strain of S. xylosus NM36 from a milk sample from a cow with chronic mastitis. In addition to robust biofilm formation, multiple antibiotic resistance phenotypes were found. To further understand the genetic background for these phenotypes, the full genome of S. xylosus NM36 was analyzed. ResultsThe genome consisted of a single circular 2,668,086 base pairs chromosome containing 32.8% G + C. There were 2454 protein-coding sequences, 4 ribosomal RNA (rRNA) genes, and 50 transfer RNA (tRNA) genes in the genome. In addition, genetic variation was studied by searching sequence data against a representative reference genome. Consequently, single-nucleotide polymorphism analysis was conducted and showed that there were 46,610 single-nucleotide polymorphisms (SNPs), 523 insertions, and 551 deletions. In order to overcome antibiotics, S. xylosus NM36 had been armed with several antibiotic resistance genes from several groups and families. The genome annotation service in PathoSystems Resource Integration Center (PATRIC) and Rapid Annotation using Subsystem Technology (RAST) annotation servers showed that there are multiple antimicrobial resistance elements, including antibiotic inactivation enzymes (BlaZ family, FosB), antibiotic resistance gene clusters (TcaB, TcaB2, TcaR), proteins involved in methicillin resistance (LytH, FmtA, FemC, HmrB, HmrA), TetR family transcriptional regulators, and efflux pumps conferring antibiotic resistance (NorA). In addition, we investigated and categorized the biofilm and quorum-sensing elements of the NM36 strain and found that it has multiple subsets of biofilm regulators, confirming its pathogenic nature. ConclusionsThese findings necessitate a reevaluation of microbial and clinical interventions when dealing with coagulase-negative staphylococci, particularly in the context of studies pertaining to public health. This is the first time, to our knowledge, that the entire genome of S. xylosus has been sequenced in Iraq.

I Know Something You Don’t Know: The annotation saga continues…

Over the past 20 years, the biodiversity informatics community has pursued components of the digital annotation landscape with varying degrees of success. We will provide an historical overview of the theory, the advancements made through a few key projects, and will identify some of the ongoing challenges and opportunities. The fundamental principles remain unchanged since annotations were first proposed. Someone (or something): (1) has an enhancement to make elsewhere from the source where original data or information are generated or transcribed; (2) wishes to broadcast these statements to the originator and to others who may benefit; and (3) expects persistence, discoverability, and attribution for their contributions alongside the source. The Filtered Push project (Morris et al. 2013) considered several use cases and pioneered development of services based on the technology of the day. The exchange of data between parties in a universally consistent way necessitated the development of a novel draft standard for data annotations via an extension of the World Wide Web Consortium’s Web Annotation Working Group standard (Sanderson et al. 2013) to be sufficiently informative for a data curator to confidently make a decision. Figure 2 from Morris et al. (2013), reproduced here as Fig. 1, outlines the composition of an annotation data package for a taxonomic identification. The package contains the data object(s) associated with an occurrence, an expression of the motivation(s) for updating, some evidence for an assertion, and a stated expectation for how the receiving entity should take action. The Filtered Push and Annosys (Tschöpe et al. 2013) projects also considered implementation strategies involving collection management systems (e.g., Symbiota) and portals (e.g., European Distributed Institute of Taxonomy, EDIT). However, there remain technological barriers for these systems to operate at scale, the least of which is the absence of globally unique, persistent, resolvable identifiers for shared objects and concepts. Major aggregation infrastructures like the Global Biodiversity Information Facility (GBIF) and the Distributed System of Scientific Collections (DiSSCo) rely on data enhancement to improve the quality of their resources and have annotation services in their work plans. More recently, the Digital Extended Specimen (DES) concept (Hardisty et al. 2022) will rely on annotation services as key components of the proposed infrastructure. Recent work on annotation services more generally has considered various new forms of packaging and delivery such as Frictionless Data (Fowler et al. 2018), Journal Article Tag Suite XML (Agosti et al. 2022), or nanopublications (Kuhn et al. 2018). There is risk in fragmentation of this landscape and disenfranchisement of both biological collections and the wider research community if we fail to align the purpose, content, and structure of these packages or if these fail to remain aligned with FAIR principles. Institutional collection management systems currently represent the canonical data store that provides data to researchers and data aggregators. It is critical that information and/or feedback about the data they release be round-tripped back to them for consideration. However, the sheer volume of annotations that could be generated by both human and machine curation processes will overwhelm local data curators and the systems supporting them. One solution to this is to create a central annotation store with write and discovery services that best support the needs of all stewards of data. This will require an international consortium of parties with a governance and technical model to assure its sustainability.

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Metagenomics and proteomics profiling of extracellular polymeric substances from municipal waste sludge and their application for soil and water bioremediation

This study assessed the components of anaerobically digested sludge, activated sludge, and microbial and extracellular polymeric substance (EPS) enzymes to identify the mechanisms underlying nitrogen removal and soil regeneration. 16S rRNA gene amplicon-based sequencing was used to determine the microbial community composition and the related National Center for Biotechnology Information (NCBI) protein database was used to construct a conventional library from the observed community. EPS components were identified using gel-free proteomic (Liquid Chromatography with tandem mass spectrometry-LC/MS/MS) methods. Alginate-like EPS from aerobically activated sludge have strong potential for soil aggregation and water-holding capacity, whereas total EPS from anaerobic sludge have significant potential for ammonia removal under salt stress. Fourier transform infrared spectroscopy (FTIR) revealed that both EPS may contain proteins, carbohydrates, humic compounds, uronic acid, and DNA and determined the presence of O–H, N–H, C–N, CO, and C–H functional groups. These results demonstrate that the overall enzyme activity may be inactivated at 30 g L-1 of salinity. An annotation found in Kyoto Encyclopedia of Genes and Genomes (KEGG)- KEGG Automatic Annotation Server (KAAS) revealed that the top two metabolic activities in the EPS generated from the anaerobic sludge were methane and nitrogen metabolism. Therefore, we focused on the nitrogen metabolism reference map 00910. EPS from the anaerobically digested sludge exhibited nitrate reductase, nitrite reductase, and dehydrogenase activities. Assimilatory nitrate reduction, denitrification, nitrification, and anammox removed ammonia biochemically. The influence of microbial extracellular metabolites on water-holding capacity and soil aggregation was also investigated. The KAAS-KEGG annotation server was used to identify the main enzymes in the activated sludge-derived alginate-like extracellular EPS (ALE-EPS) samples. These include hydrolases, oxidoreductases, lyases, ligases, and transporters, which contribute to soil fertility and stability. This study improves our understanding of the overall microbial community structure and the associated biochemical processes, which are related to distinct functional genes or enzymes involved in nitrogen removal and soil aggregation. In contrast to conventional methods, microbial association with proteomics can be used to investigate ecological relationships, establishments, key player species, and microbial responses to environmental changes. Linking the metagenome to off-gel proteomics and bioinformatics solves the problem of analyzing metabolic pathways in complex environmental samples in a cost-effective manner.

Genome Analysis and Optimization of Caproic Acid Production of Clostridium butyricum GD1-1 Isolated from the Pit Mud of Nongxiangxing Baijiu.

Caproic acid is a precursor substance for the synthesis of ethyl caproate, the main flavor substance of nongxiangxing baijiu liquor. In this study, Clostridium butyricum GD1-1, a strain with high caproic acid concentration (3.86 g/l), was isolated from the storage pit mud of nongxiangxing baijiu for sequencing and analysis. The strain's genome was 3,840,048 bp in length with 4,050 open reading frames. In addition, virulence factor annotation analysis showed C. butyricum GD1-1 to be safe at the genetic level. However, the annotation results using the Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server predicted a deficiency in the strain's synthesis of alanine, methionine, and biotin. These results were confirmed by essential nutrient factor validation experiments. Furthermore, the optimized medium conditions for caproic acid concentration by strain GD1-1 were (g/l): glucose 30, NaCl 5, yeast extract 10, peptone 10, beef paste 10, sodium acetate 11, L-cysteine 0.6, biotin 0.004, starch 2, and 2.0% ethanol. The optimized fermentation conditions for caproic acid production by C. butyricum GD1-1 on a single-factor basis were: 5% inoculum volume, 35°C, pH 7, and 90% loading volume. Under optimal conditions, the caproic acid concentration of strain GD1-1 reached 5.42 g/l, which was 1.40 times higher than the initial concentration. C. butyricum GD1-1 could be further used in caproic acid production, NXXB pit mud strengthening and maintenance, and artificial pit mud preparation.

Research and Application of the Internet of Things Service Platform Based on Semantic Network

In order to study the Internet of Things service platform based on semantic Network, a service-oriented clustering labeling method is proposed, firstly, a semantic Web service similarity computing method is proposed to solve the shortcomings of the existing methods. This method describes the designed semantic Web service annotation clustering algorithm based on AP algorithm. The distance matrix and related parameters of the algorithm are calculated and assigned according to the results of similarity calculation. The correctness of preference parameter selection will be compared and verified in the service discovery system. The greater the value of damping coefficient, the more iterations, and the slower the convergence rate. However, when the value of damping coefficient is 0.7, the number of iterations is moderate and the convergence rate is the best, which proves that this method has a better effect on service function matching.

DrNote: An open medical annotation service.

In the context of clinical trials and medical research medical text mining can provide broader insights for various research scenarios by tapping additional text data sources and extracting relevant information that is often exclusively present in unstructured fashion. Although various works for data like electronic health reports are available for English texts, only limited work on tools for non-English text resources has been published that offers immediate practicality in terms of flexibility and initial setup. We introduce DrNote, an open source text annotation service for medical text processing. Our work provides an entire annotation pipeline with its focus on a fast yet effective and easy to use software implementation. Further, the software allows its users to define a custom annotation scope by filtering only for relevant entities that should be included in its knowledge base. The approach is based on OpenTapioca and combines the publicly available datasets from WikiData and Wikipedia, and thus, performs entity linking tasks. In contrast to other related work our service can easily be built upon any language-specific Wikipedia dataset in order to be trained on a specific target language. We provide a public demo instance of our DrNote annotation service at https://drnote.misit-augsburg.de/.

Human and Machine Working Together towards High Quality Specimen Data: Annotation and Curation of the Digital Specimen

The engine for our Distributed System of Scientific Collections (DiSSCo) is running! Core technical components supporting this new research infrastructure are currently being implemented and the engine that will support it is already working. Even though some nuts and bolts may still be missing, we aim to show it in action to present how it will enable annotation and curation of the Digital Specimen. The Digital Specimen is a technical implementation based on FAIR Digital Objects (FAIR stands for Findable, Accessible, Interoperable and Reusable) to support the Digital Extended Specimen concept (Webster et al. 2021). We will also present and demonstrate how we will implement standardized quality checks as they are being developed in Biodiversity Information Standards (TDWG) to enhance the quality of the data. DiSSCo is currently in its preparation phase. This phase will end in January 2023 with the completion of the DiSSCo Prepare project funded by the European Commission. Part of that project is the design of the Digital Specimen infrastructure, which is not an easy task considering the wide range of use cases, stakeholders and the many possibilities it offers. However, as we are moving towards the end of the project, we have defined clear goals and priorities to give shape to that infrastructure. This is where we take a fail fast approach: to quickly implement the proposed solution and see if it really fits. One of the major needs we want to support with the Digital Specimen infrastructure (based on collected user stories (Fitzgerald et al. 2021)) is to provide services for improving the quality and usability of specimen data. Our infrastructure aims to support annotating and community-curation of the data by both machines and users. Examples of these annotations are image-based determinations, automated- or citizen science-contributed label translations or the semi-automated linking with other biodiversity data. Semi-automated linking is currently being piloted as part of the Biodiversity Community Integrated Knowledge Library project (BiCIKL) and will use a process of link prediction through artificial intelligence in combination with human validation. Improvements in data quality made together by human and machine through the curation and annotation services will help in producing a digital specimen data object with high quality, curated and extended data. As part of the presentation we aim to give a live demonstration with the first setup in which we will ingest a dataset, run standardized quality checks and automated data enrichment services. The end result will be a digital specimen that we will present in a user-friendly interface, which has been validated by quality checks and annotated by both a human and a machine. The result will also be accessible as a FAIR Digital Object through an API. During the demonstration, we aim to give the audience a clear view on how DiSSCo can help them create higher quality specimen data, and how we will benefit in this process from the outputs of the TDWG Data quality tests and assertions taskgroup.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Genome and Transcriptome Sequence Resources and Effector Repertoire of Pythium myriotylum Drechsler.

Abstract 1156: Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer

Abstract Interpreting genomic variants in tumor samples presents a challenge in research and the clinical setting. A major barrier is that information about variants is fragmented across disparate databases, and aggregating information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one stop shop for variant annotation, equipped with a powerful API for bulk annotation of variants and a user friendly interface for cancer researchers. Genome Nexus is available at https://www.genomenexus.org. It a) aggregates variant information from a large number of sources that are relevant to cancer research and clinical applications; b) allows high-performance programmatic access to the aggregated data via a unified API; c) provides a search interface and a reference page for individual cancer variants; d) provides user-friendly tools for annotating variants in patients; e) is freely available under an open source license and can be installed in a private cloud or local environment. Genome Nexus contains annotations from more than a dozen resources, including those that provide variant effect information (VEP), protein sequence annotation (Uniprot, Pfam, dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, SIFT), population prevalence (gnomAD, dbSNP, ExAC), cancer population prevalence (Cancer Hotspots, SignalDB) and clinical actionability (OncoKB, CIViC, Clinvar). The annotations can be accessed through the website, the API, and a command line client. Genome Nexus is unique in providing a user friendly interface specific to cancer that allows high performance annotation of any variant. It is the main annotation service for the popular cancer genomics tool cBioPortal, which serves thousands of users daily. It is also offered as a standalone tool for annotation, allowing researchers and clinicians as well as genomic infrastructure developers to leverage it directly in their own workflows. For example, a local installation of Genome Nexus is used for annotating all variants in AACR Project GENIE. Citation Format: Ino de Bruijn, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson, Avery Wang, Hongxin Zhang, Aaron Lisman, Adam Abeshouse, Sander Rodenburg, Sjoerd van Hagen, Remond Fijneman, Gerrit Meijer, Nikolaus Schultz, Jianjiong Gao. Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1156.

SiSEG-Auto Semantic Annotation Service to Integrate Smart Energy Data

In a modern smart energy system, the amount of available data from various sources is growing significantly. Other sectors such as medical or social sectors exhibit the same phenomenon. Due to the amount, complexity and heterogeneity of data, a complex algorithm is required for the integration and analysis of heterogeneous data sources. The Web of Things and semantic-based approaches address the fragmentation of standards, platforms, services and technologies in smart energy and non-energy sectors, and enable heterogeneous data integration and interoperability. This paper presents SiSEG, a semantic annotation service that is developed to automate the process of annotating data and address the problem of heterogeneous data integration in a reusable and extensible way by using the fuzzy method. Moreover, the accuracy of SiGEG has been evaluated.

Niche adaptation of Frankia do not drastically influence their metabolic profiling

ABSTRACT Metabolic-profiling (metabolomics/metabonomics) is a measurement strategy among biological systems for low-molecular-weight metabolites and their corresponding intermediates acting as dynamic physiological, pathophysiological and developmental stimuli. Information regarding the metabolomics of Frankia is elusive. In this study, we analyzed 48 Frankia whole-genome sequences. Kyoto Encyclopedia of Genes and Genomes (KEGG) database and KEGG Automatic Annotation Server (KAAS) were used for the prediction and functional annotation of genes associated with diverse metabolic pathways. Comprehensive codon and amino-acid usage analyses along with tRNA adaptation index and dN/dS-based evolutionary inquest were performed. We have used the Database-of-Essential-Genes (DEG) server to predict the essentiality of the metabolic pathway-related-genes. We found a strong effect of compositional constraints on the metabolic pathway genes of Frankia. The evolutionary analysis revealed the conserved nature of the studied genes. Since the metabolic pathway-related genes were found to be housekeeping genes, they are conserved and less evolved. This was further validated with a dN/dS value of less than 1. Moreover, the number of synonymous changes was more than non-synonymous changes so that the amino acid sequence remains identical. The cluster of orthologs (COG) analysis revealed “metabolism” as the main functional category of the studied gene set. The core metabolic pathway-related genes among considered Frankia strains accumulated 115 genes. This study for the first time reveals the genomic characteristics of the metabolic pathway-related genes among Frankia. From all the analysis, we could hypothesize that in spite of their host specificity and differential taxonomical distribution, Frankia maintain a core metabolic proficiency among them.

Application of image processing and convolutional neural networks for flood image classification and semantic segmentation

Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to emergency response authorities in real-time.

Annotating OGC web feature services automatically for generating geospatial knowledge graphs

AbstractThe Spatial Data Infrastructure initiatives are now broadly developed and deployed. However, while plenty of tools use them, some tasks are still complex to perform by non‐expert users, such as finding, accessing, and using some of their related OGC Web Services (OWS). One of the main reasons for these challenges is associated with semantic heterogeneity within these services. This entails a lack of proper descriptions and requires knowledge about data structure and domain‐specific query languages to discover and retrieve these services. Semantic annotations of OWS play a crucial role in achieving semantic interoperability and addressing these associated challenges. In this article we describe an approach for enabling the automatic generation of semantic annotations of Web Feature Services (WFS) at their three request levels (GetCapabilities, DescribeFeatureType, and GetFeature), which are used to generate knowledge graphs. This approach uses various external services, ontological resources (POSM, DBpedia, GeoSPARQL, GeoNames, and datos.ign.es vocabularies), and knowledge bases (DBpedia and datos.ign.es). Moreover, this approach enables us to validate annotations obtained as a previous step to generating geospatial knowledge graphs. Additionally, we present our proposal through an application case and assess it using a representative set of 21 WFS services, achieving an average of 46.70% annotations, of which 22.29% and 35.52% were validated using DBpedia and datos.ign.es initiatives, respectively. This shows the feasibility of our approach to generate knowledge graphs from WFS services in an automatic way.

Extension of the content, functionality and usability of the online FSK‐Web model repository

In December 2016 EFSA and BfR stepped into a Framework Partnership Agreement (FPA) to work jointly on the development, improvement and dissemination of global food safety tools. As outlined in the Specific Agreement #7 of this FPA one specific objective was to work towards the development of a fully operational online model repository that includes features to create, upload, download and execute models with user-defined inputs for the predefined model parameters. The user interface of the so-called FSK-Web model repository had to be designed such, that it is user-friendly and usable on various end-user devices and web browser software. The developed “FSK-Web 1.0” platform now even contains additional services, e.g. to link models with EFSA’s Knowledge Junction (KJ) or to join up to four models in a graphical user-interface (GUI) online. Detailed manuals, video tutorials and an online e-learning course were developed to support the use of the FSK-Web repository. A repository disclaimer and a model curation policy were drafted and will be released after being agreed with EFSA. The project created three key outcomes: (1) an operational curated web-based food safety model repository, and a significant number of executable QMRA models in FSKX format relevant for risk assessment; (2) improved usability and annotation of the various services offered by the FSK-Web model repository including manuals and online resources explaining the functionality and use of all aspects of the model repository; (3) provision of the possibility to link different FSKX models to create new combined models, including the option to combine models programmed in different scripting languages (also execution of combined R / Python models supported).